Fig. 3.

The REDalign architecture. a The learning network schematic encompasses feature extraction, a residual encoder-decoder network, and normalization. The RNA sequences are first transformed into an input conformation, and then fed into the deep neural network. Based on the extracted feature map, the encoder-decoder network outputs a score map for the structural alignment. b Dense Connected Module (DCM). The DCM consists of a series of BCM layers that are densely interconnected. The output feature map is formed by concatenating all feature maps from the BCM layers, including the input feature map and the output map of the encoder network. This design ensures that each layer receives all feature maps from the preceding layers, thereby improving the network’s parameter efficiency